1 Parameter section:

1.1 Background

Almost ideal tools that already exist in electronics: User provide the part’s name, the software fetch the standard model and its associated parameter, and return a complete mathematical model with all the parameters known (for instance, the PROTEL-software can easily get the released device from the NI company with all the response parameters standardized from a device library).

But currently, the knowledge and cognition level of synthetic biology do not support this: Most parts do not have a standard model associated with it. Some parameters of the parts haven’t been measured yet. Some parts with quantitative parameter values, which are highly context dependent, are hard to transfer with defined parameters across different hosts.

1.2 More considerationM

Why do we try automatic parameter fitting(or estimation, adjustment)？

• If it’s a big network with too many parameters undefined, it would get the user exhausted adjusting all the empty parameters by hand according to his/her experience and web resource. Network generated by rule based modeling is ordinarily large

• Compared to manually parameter adjustment, in silicon auto parameter adjustment is more convenient.It gives an estimation of the interest parameter value according to wet lab data, thus free the user from spending too much time on estimating a good parameter.

1.3

We adopted a demo from a Tinkercell tutorial website on implementing a simple repressilator. After the network is generated by hand, the parameters are left behind to the user to adjust by themselves. But as you can see from the following four figures, the process is tough and time consuming. (more snapshots of the process are eliminated )

zwx

………………（more trial and error）

The four figures above show a difficult parameter adjustment process by hand.

By contrast, parameters estimated by our approach in silicon can be done in a timely fashion. It’s fast and convenient(see figure below)

1.4 Algorithm

For PSO and SA algorithm detail, please go to our algorithm and technology wiki part.

The user input behavior is compared with the result generated by solving odes using cross correlation or absolute distance or fourier transform (user can select the criteria)

Part 2 Assessment section

2.1

Why do we try to evaluate a design with sensitivity and robustness analysis?

• Computer descriptions of biological mechanisms are by necessity,very simplified.

• The biology system is so complex that current mathematical description is far from accurate

• Parameters of biological parts are context dependent and some may vary drastically when host cell or environment change

• Noise come from various sources and can drastically influence the performance of a circuit

• Things that can be implemented through ode parameter adjustment might not necessarily be observed in real experiment.

• If the parameter range is slim at the working region, then most probably the desired phenomena will never be observed in wet experiment

• If we get the most sensitive parameter, we probably know what to do to refine a circuit to quick tune it to a desired behavior or just try to keep that most sensitive parameter as constant as possible.

Part3 Model reduction and network modulation (havn’t done yet)

Below is an aflow chart that demonstrates the whole process:

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